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  <h1>Source code for nlp_architect.nn.torch.modules.embedders</h1><div class="highlight"><pre>
<span></span><span class="c1"># ******************************************************************************</span>
<span class="c1"># Copyright 2017-2019 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1"># ******************************************************************************</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> <span class="k">as</span> <span class="n">nn</span>

<span class="kn">from</span> <span class="nn">nlp_architect.utils.io</span> <span class="kn">import</span> <span class="n">load_json_file</span>
<span class="kn">from</span> <span class="nn">nlp_architect.utils.text</span> <span class="kn">import</span> <span class="n">n_letters</span>


<div class="viewcode-block" id="CNNLSTM"><a class="viewcode-back" href="../../../../../tagging/sequence_tagging.html#nlp_architect.nn.torch.modules.embedders.CNNLSTM">[docs]</a><span class="k">class</span> <span class="nc">CNNLSTM</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;CNN-LSTM embedder (based on Ma and Hovy. 2016)</span>

<span class="sd">    Args:</span>
<span class="sd">        word_vocab_size (int): word vocabulary size</span>
<span class="sd">        num_labels (int): number of labels (classifier)</span>
<span class="sd">        word_embedding_dims (int, optional): word embedding dims</span>
<span class="sd">        char_embedding_dims (int, optional): character embedding dims</span>
<span class="sd">        cnn_kernel_size (int, optional): character CNN kernel size</span>
<span class="sd">        cnn_num_filters (int, optional): character CNN number of filters</span>
<span class="sd">        lstm_hidden_size (int, optional): LSTM embedder hidden size</span>
<span class="sd">        lstm_layers (int, optional): num of LSTM layers</span>
<span class="sd">        bidir (bool, optional): apply bi-directional LSTM</span>
<span class="sd">        dropout (float, optional): dropout rate</span>
<span class="sd">        padding_idx (int, optinal): padding number for embedding layers</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">word_vocab_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">num_labels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">word_embedding_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
        <span class="n">char_embedding_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span>
        <span class="n">cnn_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
        <span class="n">cnn_num_filters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
        <span class="n">lstm_hidden_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
        <span class="n">lstm_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
        <span class="n">bidir</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
        <span class="n">padding_idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">CNNLSTM</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span>
            <span class="n">word_vocab_size</span><span class="p">,</span> <span class="n">word_embedding_dims</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="n">padding_idx</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">char_embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span>
            <span class="n">n_letters</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">char_embedding_dims</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="n">padding_idx</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
            <span class="n">in_channels</span><span class="o">=</span><span class="n">char_embedding_dims</span><span class="p">,</span>
            <span class="n">out_channels</span><span class="o">=</span><span class="n">cnn_num_filters</span><span class="p">,</span>
            <span class="n">kernel_size</span><span class="o">=</span><span class="n">cnn_kernel_size</span><span class="p">,</span>
            <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span>
            <span class="n">input_size</span><span class="o">=</span><span class="n">word_embedding_dims</span> <span class="o">+</span> <span class="n">cnn_num_filters</span><span class="p">,</span>
            <span class="n">hidden_size</span><span class="o">=</span><span class="n">lstm_hidden_size</span><span class="p">,</span>
            <span class="n">bidirectional</span><span class="o">=</span><span class="n">bidir</span><span class="p">,</span>
            <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="n">num_layers</span><span class="o">=</span><span class="n">lstm_layers</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span>
            <span class="n">in_features</span><span class="o">=</span><span class="n">lstm_hidden_size</span> <span class="o">*</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">bidir</span> <span class="k">else</span> <span class="n">lstm_hidden_size</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="n">num_labels</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_labels</span> <span class="o">=</span> <span class="n">num_labels</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="o">=</span> <span class="n">padding_idx</span>

<div class="viewcode-block" id="CNNLSTM.load_embeddings"><a class="viewcode-back" href="../../../../../generated_api/nlp_architect.nn.torch.modules.html#nlp_architect.nn.torch.modules.embedders.CNNLSTM.load_embeddings">[docs]</a>    <span class="k">def</span> <span class="nf">load_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embeddings</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Load pre-defined word embeddings</span>

<span class="sd">        Args:</span>
<span class="sd">            embeddings (torch.tensor): word embedding tensor</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
            <span class="n">embeddings</span><span class="p">,</span> <span class="n">freeze</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span>
        <span class="p">)</span></div>

<div class="viewcode-block" id="CNNLSTM.forward"><a class="viewcode-back" href="../../../../../generated_api/nlp_architect.nn.torch.modules.html#nlp_architect.nn.torch.modules.embedders.CNNLSTM.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">word_chars</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        CNN-LSTM forward step</span>

<span class="sd">        Args:</span>
<span class="sd">            words (torch.tensor): words</span>
<span class="sd">            word_chars (torch.tensor): word character tensors</span>

<span class="sd">        Returns:</span>
<span class="sd">            torch.tensor: logits of model</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">word_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">char_embeddings</span><span class="p">(</span><span class="n">word_chars</span><span class="p">)</span>
        <span class="n">saved_char_size</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="mi">2</span><span class="p">]</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="n">input_size</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
        <span class="n">squashed_shape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">input_size</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
        <span class="n">char_embeds_reshape</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span>
            <span class="o">*</span><span class="n">squashed_shape</span>
        <span class="p">)</span>  <span class="c1"># (samples * timesteps, input_size)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">char_embeds_reshape</span><span class="p">)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">char_embeds</span><span class="p">)</span>
        <span class="n">char_embeds</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">char_embeds</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># global max pooling</span>
        <span class="n">new_size</span> <span class="o">=</span> <span class="n">saved_char_size</span> <span class="o">+</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span>
        <span class="n">char_features</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">new_size</span><span class="p">)</span>

        <span class="n">features</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">word_embeds</span><span class="p">,</span> <span class="n">char_features</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="o">.</span><span class="n">flatten_parameters</span><span class="p">()</span>
        <span class="n">lstm_out</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
        <span class="n">lstm_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">lstm_out</span><span class="p">)</span>
        <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">lstm_out</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">logits</span></div>

<div class="viewcode-block" id="CNNLSTM.from_config"><a class="viewcode-back" href="../../../../../generated_api/nlp_architect.nn.torch.modules.html#nlp_architect.nn.torch.modules.embedders.CNNLSTM.from_config">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_config</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">word_vocab_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Load a model from a configuration file</span>
<span class="sd">        A valid configuration file is a JSON file with fields as in class `__init__`</span>

<span class="sd">        Args:</span>
<span class="sd">            word_vocab_size (int): word vocabulary size</span>
<span class="sd">            num_labels (int): number of labels (classifier)</span>
<span class="sd">            config (str): path to configuration file</span>

<span class="sd">        Returns:</span>
<span class="sd">            CNNLSTM: CNNLSTM module pre-configured</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">config</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">FileNotFoundError</span>
        <span class="n">cfg</span> <span class="o">=</span> <span class="n">load_json_file</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">word_vocab_size</span><span class="o">=</span><span class="n">word_vocab_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span> <span class="o">**</span><span class="n">cfg</span><span class="p">)</span></div></div>


<div class="viewcode-block" id="IDCNN"><a class="viewcode-back" href="../../../../../tagging/sequence_tagging.html#nlp_architect.nn.torch.modules.embedders.IDCNN">[docs]</a><span class="k">class</span> <span class="nc">IDCNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    ID-CNN (iterated dilated) tagging model (based on Strubell et al 2017) with word character</span>
<span class="sd">    embedding (using CNN feature extractors)</span>

<span class="sd">    Args:</span>
<span class="sd">        word_vocab_size (int): word vocabulary size</span>
<span class="sd">        num_labels (int): number of labels (classifier)</span>
<span class="sd">        word_embedding_dims (int, optional): word embedding dims</span>
<span class="sd">        char_embedding_dims (int, optional): character embedding dims</span>
<span class="sd">        char_cnn_filters (int, optional): character CNN kernel size</span>
<span class="sd">        char_cnn_kernel_size (int, optional): character CNN number of filters</span>
<span class="sd">        cnn_kernel_size (int, optional): CNN embedder kernel size</span>
<span class="sd">        cnn_num_filters (int, optional): CNN embedder number of filters</span>
<span class="sd">        input_dropout (float, optional): input dropout rate</span>
<span class="sd">        word_dropout (float, optional): pre embedder dropout rate</span>
<span class="sd">        hidden_dropout (float, optional): pre classifier dropout rate</span>
<span class="sd">        blocks (int, optinal): number of blocks</span>
<span class="sd">        dilations (List, optinal): List of dilations per CNN layer</span>
<span class="sd">        padding_idx (int, optinal): padding number for embedding layers</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">word_vocab_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">num_labels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">word_embedding_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
        <span class="n">char_embedding_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span>
        <span class="n">char_cnn_filters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
        <span class="n">char_cnn_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
        <span class="n">cnn_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
        <span class="n">cnn_num_filters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
        <span class="n">input_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
        <span class="n">word_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
        <span class="n">hidden_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
        <span class="n">blocks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">dilations</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">padding_idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">IDCNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">dilations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">dilations</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_blocks</span> <span class="o">=</span> <span class="n">blocks</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span> <span class="o">=</span> <span class="n">dilations</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">=</span> <span class="n">dilations</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_labels</span> <span class="o">=</span> <span class="n">num_labels</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="o">=</span> <span class="n">padding_idx</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span>
            <span class="n">word_vocab_size</span><span class="p">,</span> <span class="n">word_embedding_dims</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="n">padding_idx</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
            <span class="n">in_channels</span><span class="o">=</span><span class="n">word_embedding_dims</span><span class="p">,</span>
            <span class="n">out_channels</span><span class="o">=</span><span class="n">cnn_num_filters</span><span class="p">,</span>
            <span class="n">kernel_size</span><span class="o">=</span><span class="n">cnn_kernel_size</span><span class="p">,</span>
            <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">)):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
                    <span class="n">in_channels</span><span class="o">=</span><span class="n">cnn_num_filters</span><span class="p">,</span>
                    <span class="n">out_channels</span><span class="o">=</span><span class="n">cnn_num_filters</span><span class="p">,</span>
                    <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                    <span class="n">padding</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">padding</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                    <span class="n">dilation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                <span class="p">)</span>
            <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span>
            <span class="n">in_features</span><span class="o">=</span><span class="p">(</span><span class="n">cnn_num_filters</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_blocks</span><span class="p">)</span> <span class="o">+</span> <span class="n">char_cnn_filters</span><span class="p">,</span>
            <span class="n">out_features</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">char_embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span>
            <span class="n">n_letters</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">char_embedding_dims</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="n">padding_idx</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">char_conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
            <span class="n">in_channels</span><span class="o">=</span><span class="n">char_embedding_dims</span><span class="p">,</span>
            <span class="n">out_channels</span><span class="o">=</span><span class="n">char_cnn_filters</span><span class="p">,</span>
            <span class="n">kernel_size</span><span class="o">=</span><span class="n">char_cnn_kernel_size</span><span class="p">,</span>
            <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">i_drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">input_dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">w_drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">word_dropout</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">h_drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">hidden_dropout</span><span class="p">)</span>

<div class="viewcode-block" id="IDCNN.forward"><a class="viewcode-back" href="../../../../../generated_api/nlp_architect.nn.torch.modules.html#nlp_architect.nn.torch.modules.embedders.IDCNN.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">word_chars</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        IDCNN forward step</span>

<span class="sd">        Args:</span>
<span class="sd">            words (torch.tensor): words</span>
<span class="sd">            word_chars (torch.tensor): word character tensors</span>

<span class="sd">        Returns:</span>
<span class="sd">            torch.tensor: logits of model</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">word_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
        <span class="n">word_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">i_drop</span><span class="p">(</span><span class="n">word_embeds</span><span class="p">)</span>
        <span class="n">word_embeds</span> <span class="o">=</span> <span class="n">word_embeds</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">conv1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">word_embeds</span><span class="p">)</span>
        <span class="n">conv1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">w_drop</span><span class="p">(</span><span class="n">conv1</span><span class="p">)</span>
        <span class="n">conv_outputs</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_blocks</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span><span class="p">)):</span>
                <span class="n">conv1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span><span class="p">[</span><span class="n">j</span><span class="p">](</span><span class="n">conv1</span><span class="p">)</span>
                <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cnv_layers</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
                    <span class="n">conv_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">conv1</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>

        <span class="n">word_features</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">conv_outputs</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>

        <span class="n">char_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">char_embeddings</span><span class="p">(</span><span class="n">word_chars</span><span class="p">)</span>
        <span class="n">saved_char_size</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="mi">2</span><span class="p">]</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="n">input_size</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
        <span class="n">squashed_shape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">input_size</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
        <span class="n">char_embeds_reshape</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">squashed_shape</span><span class="p">)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">char_conv</span><span class="p">(</span><span class="n">char_embeds_reshape</span><span class="p">)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">char_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">char_embeds</span><span class="p">)</span>
        <span class="n">char_embeds</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">char_embeds</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># global max pooling</span>
        <span class="n">new_size</span> <span class="o">=</span> <span class="n">saved_char_size</span> <span class="o">+</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span>
        <span class="n">char_features</span> <span class="o">=</span> <span class="n">char_embeds</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">new_size</span><span class="p">)</span>

        <span class="n">features</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">word_features</span><span class="p">,</span> <span class="n">char_features</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_drop</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
        <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">logits</span></div>

<div class="viewcode-block" id="IDCNN.from_config"><a class="viewcode-back" href="../../../../../generated_api/nlp_architect.nn.torch.modules.html#nlp_architect.nn.torch.modules.embedders.IDCNN.from_config">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_config</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">word_vocab_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Load a model from a configuration file</span>
<span class="sd">        A valid configuration file is a JSON file with fields as in class `__init__`</span>

<span class="sd">        Args:</span>
<span class="sd">            word_vocab_size (int): word vocabulary size</span>
<span class="sd">            num_labels (int): number of labels (classifier)</span>
<span class="sd">            config (str): path to configuration file</span>

<span class="sd">        Returns:</span>
<span class="sd">            IDCNN: IDCNNEmbedder module pre-configured</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">config</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">FileNotFoundError</span>
        <span class="n">cfg</span> <span class="o">=</span> <span class="n">load_json_file</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">word_vocab_size</span><span class="o">=</span><span class="n">word_vocab_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span> <span class="o">**</span><span class="n">cfg</span><span class="p">)</span></div>

<div class="viewcode-block" id="IDCNN.load_embeddings"><a class="viewcode-back" href="../../../../../generated_api/nlp_architect.nn.torch.modules.html#nlp_architect.nn.torch.modules.embedders.IDCNN.load_embeddings">[docs]</a>    <span class="k">def</span> <span class="nf">load_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embeddings</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Load pre-defined word embeddings</span>

<span class="sd">        Args:</span>
<span class="sd">            embeddings (torch.tensor): word embedding tensor</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
            <span class="n">embeddings</span><span class="p">,</span> <span class="n">freeze</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span>
        <span class="p">)</span></div></div>
</pre></div>

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